Liu Hailang, Wang Zhixian, Peng Ejun, Chen Zhiqiang, Tang Kun, Xia Ding
Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2021 May 31;11:679536. doi: 10.3389/fonc.2021.679536. eCollection 2021.
We aimed to determine preoperative risk factors associated with pathologic T3a (pT3a) upstaging of clinical T1 (cT1) renal cell carcinomas (RCCs) and develop a novel model capable of accurately identifying those patients at high risk of harboring occult pT3a characteristics.
A retrospective analysis of 1324 cT1 RCC patients who underwent partial nephrectomy (PN) or radical nephrectomy (RN) was performed. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with cT1 to pT3a upstaging and subsequently, those significant risk factors were used to construct models. We used the area under the curve (AUC) to determine the model with the highest discrimination power. Decision curve analyses (DCAs) were applied to evaluate clinical net benefit associated with using the predictive models.
The rates of upstaging were 6.1% (n = 81), 5.8% (n = 54) and 6.8% (n = 27) in the total population, training cohort and validation cohort, respectively. Tumor size, clinical T stage, R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior) nephrometry score, lymphocyte to monocyte ratio (LMR), prognostic nutrition index (PNI) and albumin to globulin ratio (AGR) were significantly associated with pT3a upstaging. The model that consisted of R.E.N.A.L. score, LMR, AGR and PNI achieved the highest AUC of 0.70 in the validation cohort and yielded the highest net benefit. In the subpopulation with complete serum lipid profile, the inclusion of low-density lipoprotein cholesterol (LDL-C) and Castelli risk index-I (CRI-I) significantly improved the discrimination of model (AUC = 0.86).
Our finding highlights the importance of systemic inflammation response markers and serum lipid parameters in predicting pT3a upstaging. Our model had relatively good discrimination in predicting occult pT3a disease among patients with cT1 renal lesions, and the use of the model may be greatly beneficial to urologists in risk stratification and management decisions.
我们旨在确定与临床T1（cT1）肾细胞癌（RCC）病理T3a（pT3a）分期上调相关的术前危险因素，并开发一种能够准确识别那些具有隐匿性pT3a特征高风险患者的新型模型。
对1324例行部分肾切除术（PN）或根治性肾切除术（RN）的cT1 RCC患者进行回顾性分析。研究队列按70:30的比例分为训练集和测试集以进行进一步分析。进行单变量和多变量逻辑回归分析以识别与cT1至pT3a分期上调相关的预测因素，随后，使用这些显著危险因素构建模型。我们使用曲线下面积（AUC）来确定具有最高区分能力的模型。应用决策曲线分析（DCA）来评估使用预测模型相关的临床净效益。
在总人群、训练队列和验证队列中，分期上调率分别为6.1%（n = 81）、5.8%（n = 54）和6.8%（n = 27）。肿瘤大小、临床T分期、R.E.N.A.L.（半径、外生性/内生性特征、肿瘤与集合系统或肾窦的接近程度、前后位）肾计量评分、淋巴细胞与单核细胞比率（LMR）、预后营养指数（PNI）和白蛋白与球蛋白比率（AGR）与pT3a分期上调显著相关。由R.E.N.A.L.评分、LMR、AGR和PNI组成的模型在验证队列中实现了最高AUC为0.70，并产生了最高净效益。在具有完整血脂谱的亚组中，纳入低密度脂蛋白胆固醇（LDL-C）和卡斯泰利风险指数-I（CRI-I）显著提高了模型的区分度（AUC = 0.86）。
我们的发现突出了全身炎症反应标志物和血脂参数在预测pT3a分期上调中的重要性。我们的模型在预测cT1肾病变患者隐匿性pT3a疾病方面具有相对较好的区分度，并且该模型的使用可能对泌尿外科医生进行风险分层和管理决策非常有益。
